



'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Tuple						  =					(DDIMParallelScheduler,)
						_lowerCAmelCase  : str						  =					(('eta', 0.0), ('num_inference_steps', 5_0))







						def        __lowercase     (	self :   Optional[int]					,     **lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      {
													    '''num_train_timesteps''': 10_00,
													    '''beta_start''': 0.0001,
													    '''beta_end''': 0.02,
													    '''beta_schedule''': '''linear''',
													    '''clip_sample''': True,
													}

													config.update(**lowerCAmelCase__					)
													return config







						def        __lowercase     (	self :   Tuple					,     **lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[Any]         =      10, 0.0

													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.dummy_sample_deter

													scheduler.set_timesteps(lowerCAmelCase__					)

													for t in scheduler.timesteps:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													return sample







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for timesteps in [1_00, 5_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													for steps_offset in [0, 1]:
																				self.check_over_configs(steps_offset=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Any         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config(steps_offset=1					)
													SCREAMING_SNAKE_CASE       :   Dict         =      scheduler_class(**lowerCAmelCase__					)
													scheduler.set_timesteps(5					)
													assert torch.equal(scheduler.timesteps					,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]					)					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,     [0.002, 0.02, 0.2, 2]					):
																				self.check_over_configs(beta_start=lowerCAmelCase__					,     beta_end=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													for schedule in ["linear", "squaredcos_cap_v2"]:
																				self.check_over_configs(beta_schedule=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=lowerCAmelCase__					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													for clip_sample in [True, False]:
																				self.check_over_configs(clip_sample=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													for timestep_spacing in ["trailing", "leading"]:
																				self.check_over_configs(timestep_spacing=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													for rescale_betas_zero_snr in [True, False]:
																				self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													self.check_over_configs(thresholding=lowerCAmelCase__					)
													for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "v_prediction"]:
																											self.check_over_configs(
																											    thresholding=lowerCAmelCase__					,     prediction_type=lowerCAmelCase__					,     sample_max_value=lowerCAmelCase__					,     )







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													for t in [1, 10, 49]:
																				self.check_over_forward(time_step=lowerCAmelCase__					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for t, num_inference_steps in zip([1, 10, 50]					,     [10, 50, 5_00]					):
																				self.check_over_forward(time_step=lowerCAmelCase__					,     num_inference_steps=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for t, eta in zip([1, 10, 49]					,     [0.0, 0.5, 1.0]					):
																				self.check_over_forward(time_step=lowerCAmelCase__					,     eta=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.get_scheduler_config()
													SCREAMING_SNAKE_CASE       :   int         =      scheduler_class(**lowerCAmelCase__					)

													assert torch.sum(torch.abs(scheduler._get_variance(0					,     0					) - 0.0					)					) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_20					,     4_00					) - 0.1_4771					)					) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_80					,     9_60					) - 0.3_2460					)					) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0					,     0					) - 0.0					)					) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_87					,     4_86					) - 0.0_0979					)					) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_99					,     9_98					) - 0.02					)					) < 1e-5







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config()
													SCREAMING_SNAKE_CASE       :   Dict         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      10, 0.0
													scheduler.set_timesteps(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_sample_deter
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_sample_deter + 0.1
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_sample_deter - 0.1

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      samplea.shape[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.stack([samplea, samplea, samplea]					,     dim=0					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.arange(lowerCAmelCase__					)[0:3, None].repeat(1					,     lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      model(samples.flatten(0					,     1					)					,     timesteps.flatten(0					,     1					)					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler.batch_step_no_noise(lowerCAmelCase__					,     timesteps.flatten(0					,     1					)					,     samples.flatten(0					,     1					)					,     lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Dict         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Dict         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_sum.item() - 1147.7904					) < 1e-2
													assert abs(result_mean.item() - 0.4982					) < 1e-3







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.full_loop()

													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_sum.item() - 172.0067					) < 1e-2
													assert abs(result_mean.item() - 0.22_3967					) < 1e-3







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.full_loop(prediction_type='''v_prediction'''					)

													SCREAMING_SNAKE_CASE       :   Dict         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_sum.item() - 52.5302					) < 1e-2
													assert abs(result_mean.item() - 0.0684					) < 1e-3







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													# We specify different beta, so that the first alpha is 0.99
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__					,     beta_start=0.01					)
													SCREAMING_SNAKE_CASE       :   int         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Any         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_sum.item() - 149.8295					) < 1e-2
													assert abs(result_mean.item() - 0.1951					) < 1e-3







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													# We specify different beta, so that the first alpha is 0.99
													SCREAMING_SNAKE_CASE       :   Any         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__					,     beta_start=0.01					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_sum.item() - 149.0784					) < 1e-2
													assert abs(result_mean.item() - 0.1941					) < 1e-3









'''simple docstring'''



import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCAmelCase_  :							Any             =    'http://www.mocksite.com/file1.txt'
lowerCAmelCase_  :							List[str]             =    '"text": ["foo", "foo"]'
lowerCAmelCase_  :							Any             =    '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Dict						  =					2_0_0
						_lowerCAmelCase  : int						  =					{'Content-Length': '100'}
						_lowerCAmelCase  : Optional[Any]						  =					{}







						def        __lowercase     (	self :   str					,     **lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													return [bytes(lowerCAmelCase__					,     '''utf-8'''					)]


def        UpperCAmelCase (    *A	:					int       ,		**A	:					Optional[Any]					):
							return MockResponse()
@pytest.mark.parametrize('''urls_type'''       ,		[str, list, dict]					)
def        UpperCAmelCase (    A	:					int       ,		A	:					Union[str, Any]       ,		A	:					Union[str, Any]					):
							import requests

							monkeypatch.setattr(A       ,		'''request'''       ,		A					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      URL
							if issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      url
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      [url]
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   str         =      {'''train''': url}
							SCREAMING_SNAKE_CASE       :   Any         =      '''dummy'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''downloads'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tmp_path
							SCREAMING_SNAKE_CASE       :   List[str]         =      DownloadConfig(
							    cache_dir=os.path.join(A       ,		A					)       ,		use_etag=A       ,		)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      DownloadManager(dataset_name=A       ,		download_config=A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      dl_manager.download(A					)
							SCREAMING_SNAKE_CASE       :   Dict         =      urls
							for downloaded_paths in [downloaded_paths]:
														if isinstance(A       ,		A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      [downloaded_paths]
																					SCREAMING_SNAKE_CASE       :   int         =      [urls]
														elif isinstance(A       ,		A					):
																					assert "train" in downloaded_paths.keys()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      downloaded_paths.values()
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      urls.values()
														assert downloaded_paths
														for downloaded_path, input_url in zip(A       ,		A					):
																					assert downloaded_path == dl_manager.downloaded_paths[input_url]
																					SCREAMING_SNAKE_CASE       :   Dict         =      Path(A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      downloaded_path.parts
																					assert parts[-1] == HASH
																					assert parts[-2] == cache_subdir
																					assert downloaded_path.exists()
																					SCREAMING_SNAKE_CASE       :   str         =      downloaded_path.read_text()
																					assert content == CONTENT
																					SCREAMING_SNAKE_CASE       :   str         =      downloaded_path.with_suffix('''.json'''					)
																					assert metadata_downloaded_path.exists()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      json.loads(metadata_downloaded_path.read_text()					)
																					assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type'''       ,		[str, list, dict]					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					Union[str, Any]       ,		A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      str(A					)
							if issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      filename
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      [filename]
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      {'''train''': filename}
							SCREAMING_SNAKE_CASE       :   Dict         =      '''dummy'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      xz_file.parent
							SCREAMING_SNAKE_CASE       :   List[Any]         =      '''extracted'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      DownloadConfig(
							    cache_dir=A       ,		use_etag=A       ,		)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      DownloadManager(dataset_name=A       ,		download_config=A					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      dl_manager.extract(A					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      paths
							for extracted_paths in [extracted_paths]:
														if isinstance(A       ,		A					):
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      [extracted_paths]
																					SCREAMING_SNAKE_CASE       :   str         =      [paths]
														elif isinstance(A       ,		A					):
																					assert "train" in extracted_paths.keys()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      extracted_paths.values()
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      paths.values()
														assert extracted_paths
														for extracted_path, input_path in zip(A       ,		A					):
																					assert extracted_path == dl_manager.extracted_paths[input_path]
																					SCREAMING_SNAKE_CASE       :   Any         =      Path(A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      extracted_path.parts
																					assert parts[-1] == hash_url_to_filename(A       ,		etag=A					)
																					assert parts[-2] == extracted_subdir
																					assert extracted_path.exists()
																					SCREAMING_SNAKE_CASE       :   int         =      extracted_path.read_text()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      text_file.read_text()
																					assert extracted_file_content == expected_file_content
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					int					):
							assert path.endswith('''.jsonl'''					)
							for num_items, line in enumerate(A       ,		start=1					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      json.loads(line.decode('''utf-8'''					)					)
														assert item.keys() == {"col_1", "col_2", "col_3"}
							assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl'''       ,		['''tar_jsonl_path''', '''zip_jsonl_path''']					)
def        UpperCAmelCase (    A	:					Any       ,		A	:					str					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      request.getfixturevalue(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      DownloadManager()
							for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
														_test_jsonl(A       ,		A					)
							assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl'''       ,		['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']					)
def        UpperCAmelCase (    A	:					Dict       ,		A	:					Optional[Any]					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      request.getfixturevalue(A					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      DownloadManager()
							for num_tar, (path, file) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
														for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
																					_test_jsonl(A       ,		A					)
							assert num_tar == 1
							assert num_jsonl == 2






def        UpperCAmelCase (    A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      DownloadManager()
							for num_file, file in enumerate(dl_manager.iter_files(A					)       ,		start=1					):
														assert os.path.basename(A					) == ("test.txt" if num_file == 1 else "train.txt")
							assert num_file == 2




